Multi-objective Optimization Techniques

Tarik Ahmed Rash is a professor, in the Department of computer science and Engineering, at the University of Kurdistan Hewlêr (UKH), Iraq. His areas of research cover the fields of artificial intelligence, nature-inspired algorithms, swarm intelligence, computational intelligence, machine learning, and data mining. He is a member of IEEE, Machine Intelligence Research Labs. He has authored and edited overall 117 Web of Science and Scopus publication documents, including 3 books and 27 book chapters in CRC, Springer, Elsevier, and IET. Aram Mahmood Ahmed is presently working as a lecturer and researcher, in the Department of information technology, University of Human Development, Sulaimani, Iraq. He is interested in modeling biological and natural systems into computational techniques. He is an active researcher who has published books and papers in peer-review academic journals with high impact factors. Bryar A. Hassan is currently working as an assistant professor, at Kurdistan Institution for Strategic Studies and Scientific Research, Iraq. He is interested in researching artificial intelligence, optimization, medical computing, the semantic web, and natural language processing. Most of his current research applies to designing and implementing new algorithms to evaluate their performance, accuracy, and effectiveness for various real-world applications. His research also bridges the areas of machine learning, evolutionary computing, and web science. Zaher Mundher Yaseen is an associate professor at King Fahd University of Petroleum and Minerals, Saudi Arabia. He is majoring in the field of civil engineering and its applications. He is also acting currently as research center director at Al-Ayen University, Iraq. The scope of his research is quite broad, covering water resources engineering, environmental engineering, knowledge-based system development, climate, and the implementation of data analytics and artificial intelligence. He has published over 300 research articles in international journals and a total number of citations of over 10000 (Google Scholar H-Index = 58). Seyedali Mirjalili is a professor at Torrens University Center for Artificial Intelligence Research and Optimization and is internationally recognized for his advances in nature-inspired Artificial Intelligence (AI) techniques. He is the author of more than 300 publications including five books, 250 journal articles, 20 conference papers, and 30 book chapters. With over 60,000 citations and an H-index of 80, he is one of the most influential AI researchers in the world. From Google Scholar metrics, he is globally the most cited researcher in Optimization using AI techniques, which is his main area of expertise. Nebojsa Bacanin currently works as a full professor and as a vice-rector for scientific research at Singidunum University, Belgrade, Serbia. He is involved in scientific research in the field of computer science and his specialty includes stochastic optimization algorithms, swarm intelligence, soft-computing, and optimization and modeling, as well as artificial intelligence algorithms, swarm intelligence, machine learning, image processing, and cloud and distributed computing. He has published more than 220 scientific papers in high-quality journals and international conferences indexed in Clarivate Analytics JCR, Scopus, WoS, IEEE Explore, and other scientific databases. Sinan Salih is a lecturer and scientific researcher at the Communication Technology Engineering Department, College of Information Technology, Imam Ja’afar Al-Sadiq University. He had published 60 research papers during the past few years and received hundreds of citations, with 27 H-Index on google scholar. His major research interests are including machine learning, optimization problems, nature-inspired algorithms, and enhancing their advanced versions.

1. Introduction to Metaheuristic Algorithms. 2. A Review of Recent Multi-objective Optimization Algorithms. 3. A New Binary Multi-objective Grasshopper Optimization Algorithm. 4. Multi-objective Fox Optimization Algorithms. 5. A New Multi-objective Cat Swarm Optimization Algorithm. 6. Multi-objective Ant Nesting Optimization Algorithms. 7. Advanced Hybrid Multi-objective Optimization Algorithms. 8. Multi-objective Optimization for Engineering Applications. 9. Multi-objective Optimization for Feature Selection in E-Health Applications. 10. Multi-objective Optimization for Scheduling Applications. 11. Multi-objective Optimization for Cloud, Fog, and Edge Computing. 12. Conclusion.